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Log-symmetric regression models under the presence of non-informative left- or right-censored observations

Author

Listed:
  • Luis Hernando Vanegas

    (Universidad Nacional de Colombia)

  • Gilberto A. Paula

    (Universidade de São Paulo)

Abstract

In this paper, an extension to allow the presence of non-informative left- or right-censored observations in log-symmetric regression models is addressed. Under such models, the log-lifetime distribution belongs to the symmetric class and its location and scale parameters are described by semi-parametric functions of explanatory variables, whose nonparametric components are approximated using natural cubic splines or P-splines. An iterative process of parameter estimation by the maximum penalized likelihood method is presented. The large sample properties of the maximum penalized likelihood estimators are studied analytically and by simulation experiments. Diagnostic methods such as deviance-type residuals and local influence measures are derived. The package ssym, which includes an implementation in the computational environment R of the methodology addressed in this paper, is also discussed. The proposed methodology is illustrated by the analysis of a real data set.

Suggested Citation

  • Luis Hernando Vanegas & Gilberto A. Paula, 2017. "Log-symmetric regression models under the presence of non-informative left- or right-censored observations," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 405-428, June.
    • Handle: RePEc:spr:testjl:v:26:y:2017:i:2:d:10.1007_s11749-016-0517-z
    DOI: 10.1007/s11749-016-0517-z
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    References listed on IDEAS

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    Cited by:

    1. Danúbia R. Cunha & Jose Angelo Divino & Helton Saulo, 2022. "On a log-symmetric quantile tobit model applied to female labor supply data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 49(16), pages 4225-4253, December.
    2. Helton Saulo & Alan Dasilva & Víctor Leiva & Luis Sánchez & Hanns de la Fuente‐Mella, 2022. "Log‐symmetric quantile regression models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(2), pages 124-163, May.
    3. Danúbia R. Cunha & Jose Angelo Divino & Helton Saulo, 2024. "Zero-Adjusted Log-Symmetric Quantile Regression Models," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 2087-2111, May.

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